All issues

Correct modeling of complex dynamics of urban transportation flows requires the collection of large volumes of empirical data to specify types of the modes and their identification. At the same time, setting a large number of observation posts is expensive and technically not always feasible. All this results in insufficient factographic support for the traffic control systems as well as for urban planners with the obvious consequences for the quality of their decisions. As one of the means to provide large-scale data collection at least for the qualitative situation analysis, the wide-area video cameras are used in different situation centers. There they are analyzed by human operators who are responsible for observation and control. Some video cameras provided their videos for common access, which makes them a valuable resource for transportation studies. However, there are significant problems with getting qualitative data from such cameras, which relate to the theory and practice of image processing. This study is devoted to the practical application of certain mainstream neuro-networking technologies for the estimation of essential characteristics of actual transportation flows. The problems arising in processing these data are analyzed, and their solutions are suggested. The convolution neural networks are used for tracking, and the methods for obtaining basic parameters of transportation flows from these observations are studied. The simplified neural networks are used for the preparation of training sets for the deep learning neural network YOLOv4 which is later used for the estimation of speed and density of automobile flows.

The short-term prediction of road traffic condition is one of the main tasks of transportation modelling. The main purpose of which are traffic control, reporting of accidents, avoiding traffic jams due to knowledge of traffic flow and subsequent transportation planning. A number of solutions exist — both model-driven and data driven had proven to be successful in capturing the dynamics of traffic flow. Nevertheless, most space-time models suffer from high mathematical complexity and low efficiency. Artificial Neural Networks, one of the prominent datadriven approaches, show promising performance in modelling the complexity of traffic flow. We present a neural network architecture for traffic flow prediction on a real-world road network graph. The model is based on the combination of a recurrent neural network and graph convolutional neural network. Where a recurrent neural network is used to model temporal dependencies, and a convolutional neural network is responsible for extracting spatial features from traffic. To make multiple few steps ahead predictions, the encoder-decoder architecture is used, which allows to reduce noise propagation due to inexact predictions. To model the complexity of traffic flow, we employ multilayered architecture. Deeper neural networks are more difficult to train. To speed up the training process, we use skip-connections between each layer, so that each layer teaches only the residual function with respect to the previous layer outputs. The resulting neural network was trained on raw data from traffic flow detectors from the US highway system with a resolution of 5 minutes. 3 metrics: mean absolute error, mean relative error, mean-square error were used to estimate the quality of the prediction. It was found that for all metrics the proposed model achieved lower prediction error than previously published models, such as Vector Auto Regression, LSTM and Graph Convolution GRU.

The problem of optimal control of traffic flow in an urban road network is considered. The control is carried out by varying the duration of the working phases of traffic lights at controlled intersections. A description of the control system developed is given. The control system enables the use of three types of control: open-loop, feedback and manual. In feedback control, road infrastructure detectors, video cameras, inductive loop and radar detectors are used to determine the quantitative characteristics of current traffic flow state. The quantitative characteristics of the traffic flows are fed into a mathematical model of the traffic flow, implemented in the computer environment of an automatic traffic flow control system, in order to determine the moments for switching the working phases of the traffic lights. The model is a system of finite-difference recurrent equations and describes the change in traffic flow on each road section at each time step, based on retrived data on traffic flow characteristics in the network, capacity of maneuvers and flow distribution through alternative maneuvers at intersections. The model has scaling and aggregation properties. The structure of the model depends on the structure of the graph of the controlled road network. The number of nodes in the graph is equal to the number of road sections in the considered network. The simulation of traffic flow changes in real time makes it possible to optimally determine the duration of traffic light operating phases and to provide traffic flow control with feedback based on its current state. The system of automatic collection and processing of input data for the model is presented. In order to model the states of traffic flow in the network and to solve the problem of optimal traffic flow control, the CTraf software package has been developed, a brief description of which is given in the paper. An example of the solution of the optimal control problem of traffic flows on the basis of real data in the road network of Moscow is given.